Objective To estimate the point prevalence of pneumonia and malnutrition and explore associations with household socioeconomic factors.
Design Community-based cross-sectional study conducted in January–June 2021 among a random sample of households across all villages in the study area.
Setting Kiyawa Local Government Area, Jigawa state, Nigeria.
Participants Children aged 0–59 months who were permanent residents in Kiyawa and present at home at the time of the survey.
Main outcome measures Pneumonia (non-severe and severe) defined using WHO criteria (2014 revision) in children aged 0–59 months. Malnutrition (moderate and severe) defined using mid-upper arm circumference in children aged 6–59 months.
Results 9171 children were assessed, with a mean age of 24.8 months (SD=15.8); 48.7% were girls. Overall pneumonia (severe or non-severe) point prevalence was 1.3% (n=121/9171); 0.6% (n=55/9171) had severe pneumonia. Using an alternate definition that did not rely on caregiver-reported cough/difficult breathing revealed higher pneumonia prevalence (n=258, 2.8%, 0.6% severe, 2.2% non-severe). Access to any toilet facility was associated with lower odds of pneumonia (aOR: 0.56; 95% CI: 0.31 to 1.01). The prevalence of malnutrition (moderate or severe) was 15.6% (n=1239/7954) with 4.1% (n=329/7954) were severely malnourished. Being older (aOR: 0.22; 95% CI: 0.17 to 0.27), male (aOR: 0.77; 95% CI: 0.66 to 0.91) and having head of compound a business owner or professional (vs subsistence farmer, aOR 0.71; 95% CI: 0.56 to 0.90) were associated with lower odds of malnutrition.
Conclusions In this large, representative community-based survey, there was a considerable pneumonia and malnutrition morbidity burden. We noted challenges in the diagnosis of Integrated Management of Childhood Illness-defined pneumonia in this context.
Data availability statement
Data are available upon reasonable request. Data is part of an ongoing research program.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
Reliable estimates of pneumonia incidence and prevalence from Northern Nigeria are not available, but this region has been identified as a high burden context for under-5 mortality, paediatric pneumonia mortality and malnutrition.
WHAT THIS STUDY ADDS
Based on standardised clinical assessments of a random sample of children within the community in Jigawa state, Nigeria, the point prevalence of pneumonia was 1.3% and malnutrition was 15.6%, indicating a large burden of disease.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
The current WHO Integrated Management of Childhood Illness guidelines for diagnosing pneumonia rely on the recognition of cough and/or difficulty breathing, which in this context may not be reliable and result in the underdiagnosis of pneumonia. There is a need to identify and implement interventions to improve child health in this setting given the high burden of disease.
Pneumonia remains a leading cause of child mortality globally.1 2 Nigeria has the largest absolute number of annual paediatric pneumonia deaths globally with pneumonia accounting for 20% of under-5 deaths nationally.3 4 Northern Nigeria is reported as a clear hotspot for pneumonia mortality.5 However, accurate data on the incidence, prevalence and health systems burden is limited to periodic Demographic Health Surveys (DHS) without clinical assessments (most recently in 2018 reporting 2-week pneumonia prevalence 2.6%).6 7
DHS questions have been shown to lack sensitivity in determining recent pneumonia infections and suffer from recall bias in treatments.8 Furthermore, while a number of clinical and individual risk factors (eg, age, hypoxaemia, altered conscious state, malnutrition) are associated with pneumonia morbidity and mortality, there has been much less investigation of demographic and social factors.9 Indeed, when exploring demographic and social associations with pneumonia using DHS datasets in Nigeria, our findings were inconsistent and inconclusive.6
Malnutrition is a key risk factor for poor pneumonia outcomes and premature death.9 Nigeria has one of the highest prevalence of childhood malnutrition in the Africa region,10 with an estimated 37% of children stunted and 9% malnourished and higher burden in the North.7 Demographic and social factors (eg, crowding, poverty, low maternal education) are consistently identified as risk factors for childhood malnutrition,11–13 so we wondered whether risk factors for pneumonia and malnutrition may be similar when investigated systematically within a population.
We aimed to measure the point prevalence of WHO pneumonia and malnutrition among children aged 0–59 months in Jigawa state, Nigeria, and explore socioeconomic risk factors for disease. These data will provide an objective measure of disease burden in this context and identify possible associations with under-reported household risk factors. These data will thereby support more accurate disease modelling and health service planning.
We conducted a cross-sectional household survey, January–June 2021, in Kiyawa Local Government Authority (LGA), Jigawa state, as part of the larger INSPIRING Project cluster randomised controlled trial (ISRCTN: 39213655).14
Kiyawa LGA has 11 wards and estimated population of 230 000 (57 000 aged under-5 years). It is predominantly rural, with an agricultural economy and predominantly Hausa–Fulani Muslim population. Communities live in compounds, typically comprising multiple households of extended families living together with an element of shared resources. Jigawa under-5 mortality rate is high, 192/1000 live births (2018).7
Study participants included all children aged 0–59 months residing within eligible study compounds and present at the time of the survey. Compounds were eligible if they had a resident woman aged 16–49 years old. During a formative research phase, all villages in the LGA were mapped to form a sample frame of compounds (January–March 2020). Within each village, we numbered the compounds using an Expanded Programme of Immunization approach.15 We conducted simple random sampling, proportional to cluster size (with a minimum of 50 compounds in each of the 32 study clusters), to generate a numbered list of compounds for recruitment. The target sample size was 4480 compounds, based on the primary outcome of under-5 mortality reduction for the INSPIRING trial.14
Research nurses and non-clinical data collectors had 1-week training (covering interviewing techniques, consent, study protocol, and Integrated Management of Childhood Illness (IMCI) pneumonia and anthropometry assessments), followed by supervised piloting in a neighbouring LGA. Non-clinical data collectors collected information on the socioeconomic status and compound structure through an interview with the head of the compound (or their representative in their absence). Research nurses conducted clinical screening for malnutrition and pneumonia, following the WHO IMCI 2014 guidelines, including pulse oximetry. Oxygen saturation and heart rate were measured using Lifebox oximeters (AH-MI, Acare Technology, Taiwan), with universal or paediatric clip probes attached to the child’s big toe (online supplemental appendix 1). Malnutrition was assessed through mid-upper arm circumference (MUAC) and checking for oedema. The child’s primary caregiver was asked about recent medication and care-seeking. Conducting malaria rapid diagnostic tests, or collecting invasive samples, was outside the scope of the study. Any child identified as having pneumonia or severe pneumonia was directed to the local health facility for assessment. All data was collected using a custom-built CommCare application on Android Tablets.
Pneumonia was defined according to the 2014 WHO IMCI guidelines: children with cough and/or difficult breathing and fast breathing for age and/or chest indrawing were identified as pneumonia (non-severe); or severe pneumonia if they had any signs of severe illness (online supplemental appendix 2).16 We defined hypoxaemia as peripheral oxygen saturation (SpO2)<90% and moderate hypoxaemia as SpO2 90%–93%. We recorded lung sounds with the naked ear and a stethoscope. We conducted a sensitivity analysis using a modified pneumonia definition that included children with very fast breathing (+10 breaths per minute above usual age-specific cut-off) and/or chest indrawing, irrespective of caregiver-reported cough or difficult breathing.
Nutritional status was defined using MUAC and restricted to children aged 6–59 months. We defined well-nourished as an MUAC>125 mm, moderate malnutrition as an MUAC of 115–125 mm and severe malnutrition as an MUAC of <115 mm or presence of oedema.17 Full definitions of explanatory variables are in online supplemental appendix 2. Caregivers of children with observed danger signs were informed and supported to go to the nearest health facility.
The primary outcomes were the point prevalence of pneumonia (severe and non-severe combined) and malnutrition (severe and moderate combined). We described clinical presentation, compound socioeconomic characteristics, and point prevalence of pneumonia (severe, non-severe and combined) and malnutrition (severe, moderate and combined). Binary and categorical data were described using proportions, normally distributed continuous data as means and SD, and skewed continuous data using median and IQR.
We explored the association between compound socioeconomic factors (exposures) and the primary outcomes of pneumonia (non-severe and severe pneumonia combined) and malnutrition (moderate and severe malnutrition combined) using multilevel mixed effects logistic regression to adjust for compound-level clustering. We planned a sensitivity analysis using multinomial logistic regression with categorical pneumonia and malnutrition outcomes; however, given the small number of pneumonia cases we only did this for malnutrition. The selection of socioeconomic variables to include was decided a priori based on the existing literature. Biologically implausible measurements were excluded from analysis, defined as: MUAC±5 SD from the mean (n=0); oxygen saturation of <50% (n=0); we also excluded age-standardised heart rate of less than the 1st centile (n=205, 2.2%) due to likelihood of measurement error.18 We used a complete case analysis approach to missing data. Analyses conducted using Stata SE V.14 (StataCorp, College Station, Texas, USA).
Patient and public involvement
The INSPIRING Programme has taken a codesign approach, including project partners, community members and local government representatives during inception, and working with communities to finalise the larger intervention using a community conversations methodology.19 Prior to data collection, meetings with village leaders were held to explain the study and gain community consent for participation.
The study received ethical approval from University College London (reference: 3433/004), Jigawa State Ministry of Health (reference: MOH/SEC.3/S/830/1), University of Ibadan (reference: UI/EC/19/0551). Participants received study information verbally from the data collector and caregivers provided verbal consent prior to child’s assessment.
We registered 10 955 children under-5 years old in 3887 recruited compounds, of which 9171 (84%) were included in analyses—figure 1. Compounds where we did not clinically assess any children had a lower wealth quintile, poorer water and sanitation status and less crowding (table 1). We included 2–3 children (mean 2.7, range: 1–22) from each compound (mean age 24.8 months, SD: 15.8), with similar numbers of girls and boys (48.7% vs 51.3%)—table 1.
The overall pneumonia (severe and non-severe) point prevalence was 1.3% (n=121/9171)—table 2. Severe pneumonia accounted for 45% of cases, with point prevalence of 0.6% (n=55/9171). We observed minimal difference in pneumonia prevalence by child age (1.7%: <2 months; 1.3%: 2–11 months and 12–59 months; p=0.785) or sex (1.3% for both, p value=0.838). In compounds where more than one child was assessed, 0.4% had more than 1 case of pneumonia diagnosed (n=9/2356; intracluster correlation, ICC=0.388).
We obtained successful SpO2 measurement from 95.5% of children; 0.1% (n=8/9171) had hypoxaemia (SpO2<90%), 2.0% (n=187/9171) moderate hypoxaemia (SpO2 90–93%). Biologically implausible measurements (n=205) were higher in younger children (8.7%: aged<2 months; 5.2%: 2–11 months; 1.1%: 12–59 months; p value<0.001) and in children who were agitated/crying vs calm/sleeping (3.1% vs 2.0%; p value=0.006). No children with SpO2<90% (n=8) met the WHO pneumonia definition (online supplemental appendix 3); 8/187 (4.3%) children with SpO2 90–93% had WHO-defined pneumonia. Abnormal respiratory sounds were recorded in 4.1% (n=371/9171) of children, much more frequently in those with pneumonia (non-severe 42.4%, n=28/66; severe 56.4%, n=31/55) than those without (3.5%, n=312/9050; p value<0.001)—figure 2.
Fast breathing was common (12.3%), with most of these children not having caregiver reported cough and/or difficulty breathing (n=1061/1127, 94.1%). Of these non-pneumonia fast breathers, 92.7% (n=984/1061) did not have any other signs of acute illness and their median respiratory rate was 60 (<2 months), 52 (2–11 months) and 42 (12–59 months) breaths per minute. Very fast breathing was measured in 1.6% (n=150/9171) of children. Using our alternative pneumonia definition—very fast breathing and/or chest indrawing regardless of cough/difficult breathing, we classified 2.8% (n=258/9171) of children with pneumonia (0.6% severe pneumonia, 2.2% non-severe pneumonia—online supplemental appendix 4).
Malnutrition prevalence among children aged 6–59 month was 15.6% (11.4% moderately and 4.1% severely malnourished—table 2). Both moderate (22.2% vs 9.9%) and severe malnutrition (6.9% vs 3.7%, p value<0.001) were more common in children aged 6–11 month than in children aged 12–59 months respectively, and girls than boys (17.1% vs 14.2%, respectively, p value=0.001). In compounds where more than one child was assessed, 10.1% (n=238/2356, ICC=0.463) had more than one malnourished child.
Care-seeking and treatment
In 2 weeks prior to the survey, 12.3% of children had taken some form of medication and 0.5% had been admitted to hospital (including 1/55, 1.8%, of those with severe pneumonia) (table 3). Compared with well children (12.0%), recent treatment was higher among children diagnosed with non-severe pneumonia (24.2%) or severe pneumonia (49.1%, p value<0.001). A similar pattern was seen for malnutrition, fever and hypoxaemia—table 3.
Associations with compound factors
In the pneumonia model, having access to any toilet facility was associated with lower odds of pneumonia (severe or non-severe) (aOR: 0.56; 95% CI: 0.31, 1.01) and having any type of education had higher odds of pneumonia than none. In the malnutrition model, being older (aOR: 0.22; 95% CI: 0.17 to 0.27), male (aOR: 0.77; 95% CI: 0.66 to 0.91) and having a head of compound as a business owner or professional (vs subsistence farmer, aOR 0.71; 95% CI: 0.56 to 0.90) were associated with lower odds of malnutrition—table 4. Using a multinomial model for nutritional status, the findings were similar (online supplemental appendix 5).
In this large, representative community survey in a semirural community in northern Nigeria, we measured a pneumonia point prevalence of 1.3%, a malnutrition prevalence of 15.6% and found that 12.3% of children had received some form of medication in the prior 2 weeks. This represents a considerable morbidity burden within the community and a high rate of medication use in children under-5.
Our pneumonia point-prevalence estimate is similar to the 2-week prevalence reported in the 2018 DHS (2.6%). Assuming an average disease duration of 5 days, this translates to an annual pneumonia incidence of 963 per 1000 children, similar to estimates from humanitarian settings (730 to 1460 per 1000 patient-years).7 20 Almost half the pneumonia cases met criteria for severe pneumonia, suggesting substantial under-recognition of non-severe cases. Additionally, many children with very fast breathing or chest indrawing were not classified as pneumonia because they lacked caregiver-reported cough or difficult breathing. When using these signs in an alternative pneumonia definition that did not rely on caregiver recognition of symptoms, the point-prevalence doubled (2.8%). Given low caregiver knowledge and understanding of pneumonia in this northern Nigerian context,21 and poor agreement between caregiver reported signs and clinical assessment,8 22 the WHO IMCI guideline’s reliance on caregiver reported cough/difficult breathing is likely missing true pneumonia cases.
Given the large numbers of additional children with slightly fast-breathing and no other signs of illness, and the challenge of respiratory rate assessments,23 expanding the pneumonia definition to include fast-breathing alone would likely lead to overtreatment. However, ‘noisy breathing’ may be a useful additional clinical indicator for pneumonia in community and primary care settings.
As expected, we found lower prevalence of hypoxaemia (0.1%) and moderate hypoxaemia (2.0%) than reports from primary care and community clinic contexts.24–27 However, most of the hypoxaemic children did not have obvious signs of illness, suggesting issues in the accuracy and quality of measurements (despite removing biologically implausible measurements) or missed serious disease. For example, congenital heart disease is often asymptomatic and undiagnosed, with Nigerian data suggesting particularly high prevalence among children with pneumonia.28 Pulse oximetry screening in low-prevalence populations needs to consider the potential for, and impact of, false positives (and false negatives).29
We found substantially higher rates of malnutrition among girls than boys (21% higher), possibly related to gendered provision of food and care-seeking. A pooled analysis of care-seeking for children across sub-Saharan Africa reported an 11% increased odds in care-seeking for boy children with diarrhoea,30 but studies from Nigeria have found mixed results around care-seeking and child sex.31–33 A more in-depth contextual understanding is needed of this finding.
We found few associations between sociodemographic factors and pneumonia or malnutrition, and no association with socioeconomic factors we might be expected (eg, crowding or lower socioeconomic status)—similar to previous analysis of 2018 DHS data.6 We did not explore maternal factors and had fewer children from the poorest compounds, so may be missing key sociodemographic relationships. An interesting finding was the association between malnutrition and subsistence farming. Compounds that rely on subsistence farming for food and income may be more vulnerable to food insecurity and shocks (eg, flooding, drought).34 35 Additionally, animal husbandry has previously been associated with malnutrition in Northern Nigeria, potentially through increasing exposure to diarrhoeal infections.36
While rates of recent medication usage were high, only 1/55 (1.8%) children with severe pneumonia (warranting hospital admission) had been admitted, suggesting a gap between access to medication and access to formal health services, particularly hospital care.37 Studies from Nigeria and elsewhere have identified a range of cultural, physical and resource-related barriers to accessing care38 and shown that these barriers are associated with higher risk of child death.39 We have previously explored these barriers for children with pneumonia in Lagos and Jigawa states, Nigeria,38 and these new findings add further urgency to efforts to improve access.
We had planned to assess malnutrition through weight-for-age z-scores and include children<6 months. However, nearly half of the children were missing a valid weight measurement so we chose not to analyse these data. Practical problems with faulty scales (Seca 354 digital scales) and switching between metric and imperial settings, highlights the challenges of community-based nutrition assessments. We had a large sample size but a small number of severe pneumonia cases, preventing planned secondary analysis using multinomial or ordinal regression. We combined pneumonia and severe pneumonia into a binary outcome but this may have masked important associations between sociodemographic factors and severe illness. In northern Nigeria, pneumonia case numbers are typically higher in the dusty Harmattan season (particularly the cooler, drier Jan-Feb period) and in the peak of the wet season (August),40 although data regarding this is mixed.6 Our data reflects this pattern, with higher pneumonia numbers and incidence in January that later months (online supplemental appendix 6). While we do not expect our findings to have varied substantially with a longer sampling frame, climatic and other contextual factors make our prevalence estimates more relevant to other semirural contexts in the lower Saharan region of Africa.
In this large representative community-based survey, we found a high pneumonia and malnutrition morbidity burden. WHO guidelines for diagnosing pneumonia rely on caregiver recognition of cough and/or difficulty breathing, which in this context may not be reliable and likely results in underdiagnosis of pneumonia.
Data availability statement
Data are available upon reasonable request. Data is part of an ongoing research program.
Patient consent for publication
This study involves human participants. The study was approved by University College London (reference: 3433/004), Jigawa State Ministry of Health (reference: MOH/SEC.3/S/830/1) and University of Ibadan (reference: UI/EC/19/0551) research ethics committees. Participants gave informed consent to participate in the study before taking part.
We would also like to acknowledge the communities and community leaders for their engagement with the project.
Twitter @CarinaTKing, @tinylungsglobal
CK and MS contributed equally.
HRG and AGF contributed equally.
Collaborators INSPIRING Consortium: Carina King (Karolinska); Tim Colbourn, Rochelle Ann Burgess, Agnese Iuliano (UCL); Hamish R Graham (Melbourne); Eric D McCollum (Johns Hopkins); Tahlil Ahmed, Samy Ahmar, Christine Cassar, Paula Valentine (Save the Children UK); Adamu Isah, Adams Osebi, Ibrahim Haruna, Abdullahi Magama, Ibrahim Seriki (Save the Children Nigeria); Temitayo Folorunso Olowookere (GSK Nigeria); Matt McCalla (GSK UK); Adegoke G Falade, Ayobami Adebayo Bakare, Obioma Uchendu, Julius Salako, Funmilayo Shittu, Damola Bakare, and Omotayo Olojede (University of Ibadan).
Contributors CK, TC, RAB, HRG, EDM, AAB, AGF, OU and JS designed the study. AAB, DB, TO, JS, OA, SA and TA contributed to implementation. TC, CK and AGF were grant holders for the evaluation component. The manuscript was drafted by CK and MS, with substantial input from HRG. All authors contributed to and approved the final manuscript. CK is guarantor.
Funding This work was funded through the GlaxoSmithKline (GSK)–Save the Children partnership (grant reference: 82603743). Employees of both GSK and Save the Children contributed to the design and oversight of the study as part of a codesign process. Any views or opinions presented are solely those of the author/publisher and do not necessarily represent those of Save the Children or GSK, unless otherwise specifically stated.
Competing interests None.
Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.